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Article

Automated Deep Learning Phenotyping of Tricuspid Regurgitation in Echocardiography.

JAMA cardiology - 1 Jun 2025

Vrudhula Amey, Vukadinovic Milos, Haeffele Christiane, Kwan Alan C, Berman Daniel, Liang David, Siegel Robert, Cheng Susan, Ouyang David

Abstract excerpt

Importance: Accurate assessment of tricuspid regurgitation (TR) is necessary for identification and risk stratification. Objective: To design a deep learning computer vision workflow for identifying color Doppler echocardiogram videos and characterizing TR severity. Design, Setting, and Participants: An automated deep learning workflow was developed using 47 312 studies (2 079 898 videos) from Cedars-Sinai...

Read the complete abstract on PubMed

Topics

Humans
Tricuspid Valve Insufficiency
Deep Learning
Male
Female
Middle Aged
Severity of Illness Index

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Aged

Phenotype

ROC Curve

Echocardiography, Doppler, Color

Source

Open in PubMed Open full text

Identifiers

PMID: 40238103
DOI: 10.1001/jamacardio.2025.0498
PMCID: PMC12004246

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 25 Oct 2026.

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